Fungi Active Microbial Metabolism Detection of Rhizopus Sp

chemosensors

Article Fungi Active Microbial Metabolism Detection of Rhizopus sp. and Aspergillus sp. Section Nigri on Strawberry Using a Set of Chemical Sensors Based on Carbon Nanostructures

Marcia W. C. C. Greenshields 1, Bruno B. Cunha 1, Neil J. Coville 2, Ida C. Pimentel 3, Maria A. C. Zawadneak 3, Steffani Dobrovolski 3, Mireli T. Souza 3 and Ivo A. Hümmelgen 1,* 1 Departamento de Física, Universidade Federal do Paraná, Caixa Postal 19044, 81531-980 Curitiba, Brazil; marcia@ﬁsica.ufpr.br (M.W.C.C.G.); [email protected] (B.B.C.) 2 DST-NRF Centre of Excellence in Strong Materials and Molecular Sciences Institute, School of Chemistry, University of the Witwatersrand, PO Wits, 2050 Johannesburg, South Africa; [email protected] 3 Departamento de Patologia Básica, Laboratório de Microbiologia e Biologia Molecular-LabMicro, Universidade Federal do Paraná, 81531-980 Curitiba, Brazil; [email protected] (I.C.P.); [email protected] (M.A.C.Z.); [email protected] (S.D.); [email protected] (M.T.S.) * Correspondence: iah@ﬁsica.ufpr.br; Tel.: +55-41-3361-3645

Academic Editor: Igor Medintz Received: 20 April 2016; Accepted: 6 September 2016; Published: 14 September 2016 Abstract: We use a set of three resistive sensors based on undoped multi-walled carbon nanotubes, B-doped multi-walled carbon nanotubes, and N-doped multi-walled carbon nanotubes to study fungal infection in strawberries inoculated with Rhizopus sp. or with Aspergillus sp. section Nigri. We apply tristimulus analysis using the conductance variation of the sensors when exposed to the infected strawberries to distinguish between uninfected strawberries and strawberries infected with Rhizopus sp. or with Aspergillus sp. section Nigri, and to obtain a graphical representation providing a tool for the simple and fast detection and identiﬁcation of the fungal infection.

Keywords: Rhizopus; Aspergillus; fungi microbial metabolism; sensors; carbon nanostructures

1. Introduction The strawberry fruit is pleasant to the eye and to taste. It has a characteristic flavor, a bright red color, and good consumer acceptance. It can be consumed in natura, or in the form of candies and beverages, having a large market. Strawberries are non-climacteric fruits [1,2], having a slow and steady decline in post-harvest respiratory rate. They only ripen on the plant and do not produce ethylene after harvest, constituting a highly perishable product. They present a limited post-harvest life, different from climacteric fruits that react in the presence of ethylene, maturing even after being separated from the plant and producing ethylene at high rates [3]. If strawberries are harvested in an advanced maturation state, they may be difficult to commercialize because of decomposition and rot; if they are harvested before maturation and without post-harvest ripening, they may show high acidity, astringency, and no flavor [4,5]. After harvesting, careful planning is also required during storage and transport to ensure that the fruits are not degraded by microorganisms [6–8]. Some factors that influence strawberry quality after harvest are related to physical, physiological, and pathological damage which may occur during transport [5,9] and storage in the markets, or in the hands of consumers [10–12]. Physical damage found in fruits can be the gateway to many pathogens (mainly fungi [13]), causing post-harvest rot [11,12]. Additionally, strawberries constitute an excellent substrate for the development of fungi [14] due to the low pH, high water content, as well as the nutritional composition of the fruit, which consists of sugars, acids, and vitamins. Fungi begin their

Chemosensors 2016, 4, 19; doi:10.3390/chemosensors4030019 www.mdpi.com/journal/chemosensors Chemosensors 2016, 4, 19 2 of 9 colonization by feeding on these nutrients, causing the putrefaction of the fruit, which suffers intense metabolic activity as it matures [15–17]. Strawberries are commonly susceptible to fungi such as Botrytis sp., Penicillium sp., Phomopsis sp. [18], Aspergillus sp. section Nigri [19], and Rhizopus sp. [20]. Several tests and studies have been conducted on strawberries in order to expand and optimize the strawberry life cycle [21], because the appearance of fungal infection directly affects the commercial value of strawberries, constituting a major cause of fruit rejection and hence reducing the supply to the consumer [22,23]. It is common for fungi to produce volatile organic compounds (VOCs) during fruit infection [24]. These gaseous compounds are made up of various carbon-based mixtures [6,13,16]. Researchers have identified about 250 VOCs released due to fungi metabolism during colonization, when fungi feed on nutrients found in fruit [16,25]. The main VOCs found in this rotting process are iso-amylalcohol, 1-octen-3-ol, and numerous other eight-membered carbon ketones and alcohols [2,9,19,22,25,26]. The discovery of simple procedures to make a range of novel carbon nanostructures has led researchers to use these new materials to develop organic electronic devices [27–29], including chemical sensors [30] based on these carbons. The use of carbon nanostructure-based sensors [31,32] to target fruit VOCs constitutes an opportunity to develop strategies to detect strawberry fungi-related VOCs, and to perform real-time monitoring of the evolution of fungal infection in strawberries. The objective of this work is to demonstrate a potentially inexpensive, fast, and highly-sensitive set of unspecific chemical sensors and subsequent related data analysis procedures that are able to detect the appearance of the fungus at an early stage in strawberry ripening. This may allow, for example, the withdrawal of batches of infected fruits before contamination dissemination, and thus the ability to timely determine anti-fungicidal applications and the prediction of the ideal harvesting time. The availability of such a technology may contribute to a reduction in post-harvest losses. In this study, we investigate the chemical sensor response based on three different carbon nanostructures (CNSs) that can identify the possible appearance of fungus on the strawberry fruits. They are: multi-walled carbon nanotubes (MWCNTs), multi-walled carbon nanotubes doped with nitrogen (N-MWCNTs) [31], and multi-walled carbon nanotubes doped with boron (B-MWCNTs), [33]. We identified two genera of fungi: Aspergillus sp. section Nigri and Rhizopus sp., which in the study will be denoted simply as Aspergillus and Rhizopus, respectively. The mathematical method used to graphically present the data is based on tristimulus analysis [34].

2. Materials and Methods The MWCNTs, N-MWCNTs, and B-MWCNTs used in this study have an inner diameter of ~10 nm and an outer diameter of around 20–30 nm. Their synthesis and characterization procedures have been reported elsewhere [31,32,34,35]. The composites used in the sensors were prepared using one of the CNSs dispersed in a 1.3 × 105 Da poly(vinyl alcohol) (PVA) matrix. The dispersion was prepared by adding 6 mg/mL hexadecyltrimethylammonium bromide (CTAB) and 4 mg/mL of one of the CNSs in water [33], using a procedure similar to that reported elsewhere [35–38]. The dispersion was ultrasonicated for 30 min at room temperature and sequentially, for 60 min at 0 ◦C. It was then left for 4 days at ~0 ◦C, which is ◦ below the Kraft temperature (TK = 25 C) of CTAB [39], allowing for the precipitation of the excess surfactant in the form of hydrated crystals. In the sequence, 50% of the total volume of the supernatant was removed and mixed with 6 mg/mL of PVA in water, giving a ﬁnal CNS weight content in PVA of 87%. The mixture was then stirred for 2 h at a temperature below TK, thus avoiding the formation of CTAB micelles. We used interdigitated ENIG (Electroless Nickel Immersion Gold) electrodes (nine pairs; 7.5 mm long, with a gap of 0.3 mm between electrode strips) patterned on a FR4 epoxy resin/ﬁber glass board, supplied by Micropress SA (see Figure1a). Interdigitated electrodes were sequentially cleaned in acetone, isopropanol, and ultrapure water (20 min each) in an ultrasonic bath and dried in air stream. Chemosensors 2016, 4, 19 3 of 9 Chemosensors 2016, 4, 19 3 of 9

FigureFigure 1.1. (a) Interdigitated electrodes with multi-walled carbon nanotubes (MWCNTs) ﬁlm;film; ((bb)) greengreen strawberries;strawberries; andand ((cc)) strawberriesstrawberries inoculatedinoculated withwith AspergillusAspergillus..

Sensors were prepared by simply dropping 10 μL of a CNS–PVA dispersion in water on top of Sensors were prepared by simply dropping 10 µL of a CNS–PVA dispersion in water on top of an interdigitated electrode. After deposition on top of the substrate, the dispersion was annealed for an interdigitated electrode. After deposition on top of the substrate, the dispersion was annealed for 30 min at 50 °C to completely evaporate the solvent. 30 min at 50 ◦C to completely evaporate the solvent. In this study, we used chemical sensors to monitor six green strawberries (Fragaria x ananassa) In this study, we used chemical sensors to monitor six green strawberries (Fragaria x ananassa) (see Figure 1b), for the detection of Aspergillus and Rhizopus fungal infestation. The strawberries were (see Figure1b), for the detection of Aspergillus and Rhizopus fungal infestation. The strawberries were harvested in organic strawberry fields, located in Pinhais, Paraná state, Brazil (25°23’S, 49°07′W). harvested in organic strawberry ﬁelds, located in Pinhais, Paraná state, Brazil (25◦230S, 49◦070W). Strawberries were sequentially washed in water, ethanol (50%) for 1 min each, and 1% sodium Strawberries were sequentially washed in water, ethanol (50%) for 1 min each, and 1% sodium hypochlorite solution in sterile water for 30 s to remove any microorganisms present during hypochlorite solution in sterile water for 30 s to remove any microorganisms present during harvesting. harvesting. We began monitoring the green strawberries on the same day as the harvesting occurred. We began monitoring the green strawberries on the same day as the harvesting occurred. After the After the measurements of the organic green strawberries (used as reference), they were transported measurements of the organic green strawberries (used as reference), they were transported to the to the microbiology laboratory to inoculate with the fungi (Aspergillus or Rhizopus, see Figure 1c). microbiology laboratory to inoculate with the fungi (Aspergillus or Rhizopus, see Figure1c). For the inoculation process, the fungi were transferred and placed in glass Petri dishes For the inoculation process, the fungi were transferred and placed in glass Petri dishes measuring measuring 9 cm in diameter, containing on average 20 mL of potato dextrose agar for spore growth. 9 cm in diameter, containing on average 20 mL of potato dextrose agar for spore growth. Subsequently, Subsequently, the plates were incubated for seven days in a BOD (biochemical oxygen demand) the plates were incubated for seven days in a BOD (biochemical oxygen demand) chamber and chamber and subjected to a temperature of 28 ± 2 °C at a relative humidity of 70% ± 10% and 14 h subjected to a temperature of 28 ± 2 ◦C at a relative humidity of 70% ± 10% and 14 h photophase. photophase. After these seven days, the mycelium was then scraped from the Petri dishes with the After these seven days, the mycelium was then scraped from the Petri dishes with the help of sterile help of sterile material, and the fungal spore solution was prepared by adding 20 mL of sterile material, and the fungal spore solution was prepared by adding 20 mL of sterile distilled water in distilled water in a tube containing Tween 80® sterile solution. After the scraping process, the solution a tube containing Tween 80® sterile solution. After the scraping process, the solution underwent a underwent a screening procedure using sterile gauze. Then, the number of spores of Rhizopus and screening procedure using sterile gauze. Then, the number of spores of Rhizopus and Aspergillus were Aspergillus were counted with the help of a Neubauer chamber, giving counts of counted with the help of a Neubauer chamber, giving counts of 3.2 × 107, and 1.3 × 108 spores/mL−1, 3.2 × 107, and 1.3 × 108 spores/mL−1, respectively. For the fungal inoculation, the fruits were fully respectively. For the fungal inoculation, the fruits were fully immersed in the respective fungi spore immersed in the respective fungi spore solutions for 3 min. Immediately after drying, three fruits solutions for 3 min. Immediately after drying, three fruits were inoculated with each fungus and were inoculated with each fungus and placed in a beaker coated with polyethylene terephthalate placed in a beaker coated with polyethylene terephthalate (PET) ﬁlm (Figure1c). Micrographs of (PET) film (Figure 1c). Micrographs of Rhizopus sp. and Aspergillus sp. section Nigri fungi can be seen Rhizopus sp. and Aspergillus sp. section Nigri fungi can be seen in Figure2. in Figure 2.

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Figure 2. Micrographs of (a) Rhizopus sp.; and (b) Aspergillus sp. section Nigri fungi. Figure 2. Micrographs of (a) Rhizopus sp.; and (b) Aspergillus sp. section Nigri fungi.

Electrical measurements were made using an Agilent 4284 LCR meter. All conductance measurementsElectrical were measurements made at a frequency were made of using27 kHz an and Agilent a voltage 4284 amplitude LCR meter. of 500 AllmV. conductance measurementsTemperature were and made relative at a frequencyhumidity ofwere 27 kHzmonito andred a voltagewith a Minipa amplitude MT-241 of 500 sensor mV. [34,35]. For the electricalTemperature measurements, and relative nine humidity sensors were (three monitored prepared with with a Minipa each of MT-241 the CNSs) sensor were [34 ,fixed35]. For in the electricalscrew cap measurements, of a glass recipient nine (see sensors Figure (three 3) and prepared exposed with to air each for of 60 the s for CNSs) the stabilization were fixed inof the screw cap of a glass recipient (see Figure3) and exposed to air for 60 s for the stabilization of the chemical sensor response and determination of the conductance base line (G0). Then, the sensors were chemicalexposed for sensor 240 responses to volatile and organic determination compounds of the ex conductancehaled by the base three line strawberry (G0). Then, fruits the sensors inoculated were exposedwith Rhizopus for 240 and s toplaced volatile inside organic the 3200 compounds mL glass chamber. exhaled by The the procedure three strawberry was repeated fruits for inoculated the three withstrawberryRhizopus fruitsand inoculated placed inside with the Aspergillus 3200 mL. glassSimilar chamber. measurements The procedure were also was made repeated once for using the threechambers strawberry containing fruits only inoculated the fungal with colonies.Aspergillus In .these Similar experiments, measurements the screw were alsocap madewas fixed once on using the chambersglass, exposing containing the sensor only to the the fungal inner colonies. environment In these containing experiments, the frui thets screwor fungal cap colonies was fixed until on the glass, exposing the sensor to the inner environment containing the fruits or fungal colonies until the conductance saturated (corresponding to Gmax). After 240 s, the screw cap was removed, exposing the conductancesensor to air for saturated 60 s to allow (corresponding sensor recovery to Gmax and). Afterfurther 240 confirmation s, the screw of cap the was baseline. removed, The exposingconductance the sensordata were to air collected for 60 s in to the allow complete sensor recoverysequence and of measurements, further confirmation as exemplified of the baseline. in Figure The 4. conductance dataEach were measurement collected in the was complete repeated sequence three times of measurements, in sequence for as exemplifiedthe nine sensors, in Figure and4 the. average valueEach for each measurement type of sensor was repeatedwas recorded. three timesThis procedure in sequence was for repeated the nine sensors,over 20 days, and the during average the valueprogress for of each the typestrawberry of sensor fungi was infection. recorded. The This fruits procedure were maintained was repeated inside over the 20closed days, chamber during thefor progressthe duration of the of strawberrythe 20 days, fungi except infection. during measurement, The fruits were when maintained the screw inside cap thewas closed changed. chamber for the durationControl ofmeasurements the 20 days, exceptwith interdigitated during measurement, electrodes when without the screwdeposited cap wasCNSs changed. were also made, to exclude the possibility of response occurrence in the absence of CNSs.

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Control measurements with interdigitated electrodes without deposited CNSs were also made, to Chemosensors 2016, 4, 19 5 of 9 excludeChemosensors the 2016 possibility, 4, 19 of response occurrence in the absence of CNSs. 5 of 9

Figure 3. Array of sensors placed in the cap. Figure 3. Array of sensors placed in the cap. Figure 3. Array of sensors placed in the cap.

60 Sensor 1- MWCNTs 60 Sensor 1- MWCNTs Sensor 2- N-MWCNTs Sensor 2- N-MWCNTs Sensor 3 -B-MWCNTs Sensor 3 -B-MWCNTs

4040 ) ) S S μ ( μ ( G G 2020

00 00 50 50 100 100 150 150 200 200 250 250 300 300 tt (s)(s)

FigureFigure 4. 4. G(t)G(t) responseresponse of of the the composite-based composite-based chemical chemical sensors. sensors. At At t =t 60= 60s, the s, the sensor sensor is exposed is exposed to Figure 4. G(t) response of the composite-based chemical sensors. At t = 60 s, the sensor is exposed to theto therecipient recipient environment environment containing containing the the fruits. fruits. At Att =t 240= 240 s, s,the the sensor sensor is isexposed exposed again again to to air. air. the recipient environment containing the fruits. At t = 240 s, the sensor is exposed again to air. MWCNT:MWCNT: multi-walled multi-walled carbon carbon nanotubes; nanotubes; B-MWCNT: B-MWCNT: multi-walled multi-walled carbon carbon nanotubes nanotubes doped doped with with MWCNT: multi-walled carbon nanotubes; B-MWCNT: multi-walled carbon nanotubes doped with boron;boron; N-MWCNT: N-MWCNT: multi-walled carbon nanotubes doped with nitrogen. boron; N-MWCNT: multi-walled carbon nanotubes doped with nitrogen. 3. Results 3.3. ResultsResults

VariationVariation of of the the conductance conductance G Gas asa function a function of time oftime t wast usedwas to used calculate to calculate the response the response ∆G/G0, Variation of the conductance G as a function of time t was used to calculate the response ∆G/G0, where∆G/G 0∆,G where = (Gmax∆ G− G=0 ()/GGmax0, the− Gsubscript0)/G0, the “0” subscript indicates “0” the indicates initial value the initialof the valueconductance of the conductance G, and Gmax where ∆G = (Gmax − G0)/G0, the subscript “0” indicates the initial value of the conductance G, and Gmax G, and G indicates the maximum conductance value during the period of the measurement indicatesindicates thethemax maximummaximum conductanceconductance valuevalue duringduring thethe periodperiod ofof thethe measurementmeasurement (see(see FigureFigure 4).4). TheThe (see Figure4). The response data for the three types of sensors based either on the MWCNTs, responseresponse datadata forfor thethe threethree typestypes ofof sensorssensors basedbased eithereither onon thethe MWCNTs,MWCNTs, N-MWCNTs,N-MWCNTs, oror N-MWCNTs, or B-MWCNTs were used as input to the mathematical analysis applying the tristimulus B-MWCNTsB-MWCNTs werewere usedused asas inputinput toto thethe mathemmathematicalatical analysisanalysis applyingapplying thethe tristimulustristimulus methodology [34], employed to facilitate the visualization of data. methodologymethodology [34],[34], employemployeded toto facilitatefacilitate thethe visualizationvisualization ofof data.data. Briefly,Brieﬂy, we deﬁnedefine XG ≡≡ [∆[∆GG/G/0G]MWCNTs0]MWCNTs, YG, ≡Y [G∆≡G/G[∆0]GN-MWCNTs/G0]N-MWCNTs, and ZG, ≡ and [∆GZ/G 0≡]B-MWCNTs[∆G/G (where0]B-MWCNTs the Briefly, we define XG ≡ [∆G/G0]MWCNTs, YG ≡ [∆G/G0]N-MWCNTs, and ZG ≡ [∆G/G0]B-MWCNTs (where the (where the subscript indicates the type of CNSs used in the sensor), that were used to construct the subscriptsubscript indicatesindicates thethe typetype ofof CNSsCNSs usedused inin thethe sensor),sensor), thatthat werewere usedused toto constructconstruct thethe threethree componentsthree components of the tristimulus of the tristimulus vector X vectorG + YXGG xˆ+ +ZGYĜ. Weyˆ + thenZGzˆ. calculated We then calculated the coordinates the coordinates (xG, yG, zG) components of the tristimulus vector XG + YG + ZĜ. We then calculated the coordinates (xG, yG, zG) at the point where the tristimulus vector crossed a unitary plane, defined as xG + yG +zG = 1. These at the point where the tristimulus vector crossed a unitary plane, defined as xG + yG +zG = 1. These coordinates are given by xG = XG/(XG + YG + ZG), yG = YG/(XG + YG + ZG), and zG = ZG/(XG + YG + ZG). Since coordinates are given by xG = XG/(XG + YG + ZG), yG = YG/(XG + YG + ZG), and zG = ZG/(XG + YG + ZG). Since xG + yG + zG = 1, two of the coordinates are sufficient to univocally determine the unitary plane crossing xG + yG + zG = 1, two of the coordinates are sufficient to univocally determine the unitary plane crossing the point (xG, yG, zG) and the projection of the point on one of the planes xy, yz, or zx (see the point (xG, yG, zG) and the projection of the point on one of the planes xy, yz, or zx (see

Chemosensors 2016, 4, 19 6 of 9

(xG, yG, zG) at the point where the tristimulus vector crossed a unitary plane, deﬁned as xG + yG + zG = 1. These coordinates are given by xG = XG/(XG + YG + ZG), yG = YG/(XG + YG + ZG), and zG = ZG/ Chemosensors 2016, 4, 19 6 of 9 (XG + YG + ZG). Since xG + yG + zG = 1, two of the coordinates are sufﬁcient to univocally determine the unitary plane crossing the point (xG, yG, zG) and the projection of the point on one of the planes xy, Reference [34] for a more detailed presentation of the method). Figure 5 shows the plots yz, or zx (see Reference [34] for a more detailed presentation of the method). Figure5 shows the plots corresponding to the three possible projections. corresponding to the three possible projections.

Figure 5. ∆G/G0 responses plotted using tristimulus representation graphs (in the three possible Figure 5. ∆G/G0 responses plotted using tristimulus representation graphs (in the three possible projections)projections) forfor strawberrystrawberry deteriorationdeterioration overover 2020 daysdays followingfollowing aa fungalfungal inoculation.inoculation.

4.4. Discussion Discussion StrawberriesStrawberries exhale a large quantity of volatiles, and the response of each sensor is duedue toto thethe contributioncontribution ofof allall ofof thesethese volatiles.volatiles. No attempt has been made at this stage to investigate the speciﬁcspecific sensor–VOC interactions. In the case of a constant sensor sensitivity, variations in the concentration of these compounds will only modify the magnitude of the tristimulus vector, so that the region of intersection between the vector and the unit plane will remain the same. The intersection region will change only when the sensors are concomitantly exposed to the compounds exhaled by the fungi and the volatile compounds exhaled by the strawberries. This characteristic is useful for practical

Chemosensors 2016, 4, 19 7 of 9 sensor–VOC interactions. In the case of a constant sensor sensitivity, variations in the concentration of these compounds will only modify the magnitude of the tristimulus vector, so that the region of intersection between the vector and the unit plane will remain the same. The intersection region will change only when the sensors are concomitantly exposed to the compounds exhaled by the fungi and the volatile compounds exhaled by the strawberries. This characteristic is useful for practical applications, as it can distinguish between healthy strawberries and those infected with fungus, and can also distinguish between Rhizopus and Aspergillus infections. As can be seen in Figure5, the tristimulus coordinates of the strawberry infected with Aspergillus are separated from those infected with Rhizopus over the full 20 day measurement period. The data tend to be grouped in a region that corresponds only to the coordinates of the corresponding fungal colony. Additionally, both groups of coordinates are separated from the coordinates of uninfected strawberries, independent of the plane used for the representation, xy, yz, or zx. This separation constitutes an advantage that makes this set of sensors and the applied procedure useful as a screening tool to identify infected strawberry batches, easily allowing distinction between both types of fungal infection. In strawberry production sites, this identification may provide for the adoption of prevention strategies or even to a specific fungal species-oriented agrochemical fungicide selection. From a visual inspection of the strawberries, it was observed that from the first to the third day, there was no significant change that could indicate the development of fungus on the inoculated strawberries; on the fourth day, however, there was a sign of mycelium development on the strawberry surface, indicating deterioration by fungi. We observed that the fungus Rhizopus showed a grayish color with a dense and smooth growth on the surface, whereas Aspergillus showed a black appearance in the fruit. To test the reproducibility, all measurements were made with a second batch of strawberries, over 12 days, confirming the results obtained in the first batch.

5. Conclusions In this study, we have applied a tristimulus analysis to the set of responses of chemical sensors based on carbon nanostructures to identify fungal microorganisms (Aspergillus sp. section Nigri and Rhizopus sp.) that promote the degradation of strawberry fruits in the post-harvest stages, during storage, transportation, and sale. We have demonstrated by using tristimulus projection plots that it is possible to distinguish between strawberries infected with Aspergillus and those infected with Rhizopus. The use of the chemical sensors for the detection of early deterioration may be used as a tool to help prevent losses of stored strawberry and the further contamination of other strawberries by infected strawberries.

Acknowledgments: the authors would like to thank CNPq and PNPD/CAPES(Brazil); and NRF (South Africa) for research grants. Author Contributions: These authors contributed equally to this work. Conﬂicts of Interest: The authors declare no conﬂict of interest.

Abbreviations The following abbreviations are used in this manuscript: B-MWCNTs B-doped multiwalled carbon nanotubes BOD Biochemical oxygen demand CNSs Carbon nanostructures CTAB Hexadecyltrimethylammonium bromide ENIG Electroless nickel immersion gold MWCNTs Multiwalled carbon nanotubes N-MWCNTs N-doped multiwalled carbon nanotubes PET Polyethylene terephthalate PVA Poly(vinyl alcohol) VOC Volatile organic compounds Chemosensors 2016, 4, 19 8 of 9

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